To be more efficient in recognizing sensitive content on systems, nsfw ai systems need a rich dataset. Key data asynchronous these systems demand be fond of image and video datasets with examples of raunchy content, which allows the AI to learn what inappropriate material looks like. These datasets should be big and diverse, containing millions of labelled images and videos as per the reports from several content moderation companies. For example, in 2023 a very powerful nsfw ai model could only reach ~95% accuracy when differentiating pornography/non-pornography within datasets 100 million+ labeled images in size.
Textual data is also important along with the image and video data. Nsfw ai tools use Natural language processing (NLP) algorithms to sift through large volumes of text, including social media posts and comments as well as chats, looking for sexually explicit language or harmful discourse. Reddit, which has over 100 million posts per day uses an NLP driven nsfw ai systems to observe user posts and prevent the spreading of harmful materials. While this data is typically aggregated off of publicly available sources, consideration for user privacy and consent issues must be taken when collecting this type of information.
Additionally, nsfw ai systems have access to metadata regarding visual and textual content — things like timestamps, who uploaded what and engagement metrics. This extra layer of metadata provides a system with additional context around content to flag it more appropriately. So, if an explicit video is in a private message with the timestamp to show it is below the belt activity this might not be relevant or whatabout public content. Metadata inclusion, ensuring nsfw ai can process various content types in accordance with platform guidelines and legal obligations.
The quality and accuracy of the data labeling also impact nsfw ai systems. Having a well prepped data set, where explicit content is labelled correctly can improve the AI functionality up to 90%. One of the biggest hurdles in this process is making sure that data is labeled without bias or misinterpretation — which can be hard when you take into account how explicit content can appear differently across cultures. For example, in the 2022 YouTube content moderation controversy, incorrect labels caused the platform to wrongly flag some content while missing others, demonstrating that algorithms need access to diverse and accurate data sources.
Continuous feedback loops are also necessary for companies and organizations to build effective nsfw ai models. The system has to learn and adjust as new types of explicit content pop up. As an example, Facebook disclosed in 2021 that the technical solutions its content moderation system relied on had to be refreshed every quarter due to new types of harmful material. This means you have to keep collecting fresh data and retraining the bike — or rather, the artificial intelligence (AI) — constantly to make sure it continues to be locked in on your real world situation.
See nsfw ai for how it works and the data it needs.